MiniLM · English
Topic label matching
Rank your English topic labels against a short English passage.
MiniLM is an English embedding model, not a chatbot. First use loads about 47 MB of model/runtime data from this site before HTTP compression; transferred bytes depend on hosting and cache. Up to 40 source sentences, 800 characters per sentence and 256 model tokens per input. Short text comparison and topic matching support 800 characters. Model scores are not probabilities. Loading has a five-minute limit; inference has a two-minute limit.
Source text
Result
Choose an input, then run the tool.
Method, limits & licensing
MiniLM is an English embedding model, not a chatbot. First use loads about 47 MB of model/runtime data from this site before HTTP compression; transferred bytes depend on hosting and cache. Up to 40 source sentences, 800 characters per sentence and 256 model tokens per input. Short text comparison and topic matching support 800 characters. Model scores are not probabilities. Loading has a five-minute limit; inference has a two-minute limit.
Model sources, versions and checksumsA guide to this tool
How to use Topic label matching
- Enter English text or choose the sample.
- Set the second input and result count where available.
- Run the local model and review both the result and its score limitations.
A sample you can try
Sample input
The company shares rose after a strong earnings report.
2–12 English topic labels, one per line
business and finance sports and fitness animals and pets
What to expect
business and finance